EP2208131A1 - Encoding a hierarchical multi-layer data package - Google Patents
Encoding a hierarchical multi-layer data packageInfo
- Publication number
- EP2208131A1 EP2208131A1 EP08845183A EP08845183A EP2208131A1 EP 2208131 A1 EP2208131 A1 EP 2208131A1 EP 08845183 A EP08845183 A EP 08845183A EP 08845183 A EP08845183 A EP 08845183A EP 2208131 A1 EP2208131 A1 EP 2208131A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- data
- layer
- hierarchal
- data package
- subpackage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
Definitions
- emergency personnel receive a 200GB data dump of medical records over a network for multiple injured people. If the receiving device is in the field, the device may not have the processing power or memory to quickly and efficiently identify vital information for an injured person from the 200GBs of medical records.
- a real estate agent representing a buyer may download housing information meeting certain criteria for the buyer. However, because the information is organized from a seller's perspective, the real estate agent may miss certain listings or is unable to quickly identify information for the buyer.
- the data are less usable to the receiving device and may, in some situations, be unusable, depending on the computing resources of the receiving device.
- Figure 1 illustrates a system, according to an embodiment
- Figure 2 illustrates a hierarchical encoder, according to an embodiment
- Figure 3 illustrates a hierarchical decoder, according to an embodiment
- Figure 4 illustrates a hierarchical multi-layer data package, according to an embodiment
- Figures 5A-B illustrate a text document example of data to be coded, according to an embodiment
- Figures 6A-B and 7A-C illustrate an example of encoding the text document, according to an embodiment
- Figure 8 illustrates a flow chart of a method for encoding data, according to an embodiment
- Figure 9 illustrates a computer system, according to an embodiment.
- a hierarchical multi-layer data package is encoded.
- the hierarchical multi-layer data package also referred to as data package, is comprised of a plurality of layers arranged in a hierarchy.
- Each layer includes one or more subpackages of data comprising summaries and meta data that allows a device to quickly identify information of interest in a layer, i.e., "skim" and determine whether to decode data in the layer or whether to "drill down" to a lower layer to identify data of interest.
- decoding the data package comprises evaluating summaries and metadata in subpackages in a layer and determining whether to drill down to related subpackages in lower layers or decompress information in a current layer.
- Figure 1 illustrates a system 100 operable to code a hierarchical multilayer data package. Coding includes encoding and decoding.
- Figure 1 illustrates a server 110, a device 120 and a device 130.
- the server 110 and the devices 120 and 130 each include an information manager 140, an encoder 141, a decoder 142 and data storage 111, which are shown as 140a-c, 141a-c, 142a-c, and 11 la-c, respectively.
- the devices 120 and 130 may include devices that are operable to communicate with other devices via a network or via a peer-to-peer connection.
- the devices 120 and 130 may communicate with the server 110 via a client- server arrangement over a network, and the devices 120 and 130 may communicate with each other using a peer-to-peer protocol.
- Examples of the devices 120 and 130 may include a personal digital assistant, laptop, desktop, set top box, a vehicle including a computer system or substantially any device or apparatus including a computer system operable to perform the functions of the embodiments described herein.
- Communication between the devices 120 and 130 and the server 110 may include wired and/or wireless connections.
- Figure 1 shows the server 110 and the devices 120 and 130 to illustrate that the coding embodiments may be employed in different types of devices and in different types of networks. It will be apparent to one of ordinary skill in the art that the coding embodiments may be provided in other types of systems.
- an external data storage operating as a data repository may be used for storing data for the devices 120 and 130 or the server 140.
- the data repository does not include an information manager or an encoder or decoder.
- the devices 120 and 130 may also store information locally.
- the information manager 140 provides information to the encoder 141 to encode data for transmission to another device and also provides information to the decoder 142 to decode received data. For example, the information manager 140 maintains a list of topics of interest for the device. It also identifies the level of detail that is desired for each of these topics, e.g., "executive briefing", 500 word summary, white paper, all available raw data, etc. The information manager 140 also maintains current information about the state of computing resources, e.g., the processor utilization, the free memory space, etc. Using this information, the information manager 140 makes coding decisions. For example, the information manager 140 provides the encoder 141 with a compression ratio that represents the best trade-off between data package size and ease of use. One embodiment generates such advice based not only on the current computing resource measurements for the device, but also on future resource usage predictions for the device and other devices in its network.
- the information manager 140 determines the hierarchical compression strategy for encoding the data. This may includes the compression ratio, the maximum number of subpackages of data for a given layer in a data package and other metadata.
- the maximum number of subpackages may be a function of the number of statistically significant/different data clusters, as well as the status of available computing resources and the "operations goal" of the network of devices which share data packages.
- the "operations goal” may be based on the attributes of the computing resources for the anticipated set of devices which will transmit and/or use the data package. For example, portable devices with less memory and processing power may set goals that best utilize their computing resources. In general, more clusters will use more sub-packages, thereby increasing the specificity of the data in the sub-package.
- the information manager 140 also determines the maximum and target number of layers in the data package. This is a function of the overall size of the data package, as well as the status of available computing resources and the "operations goal" of the network of devices which share data packages. In general, larger data packages may use more layers, thereby reducing the amount of data that needs to be scanned at the top layer. Higher compression rates and computational efficiencies can be obtained with larger data packages. Therefore, in on embodiment, the largest data sub-packages possible are used at each level in the hierarchy. This is consistent with the "burst" (transmission) and "skim” (search) approach.
- the encoder 141 is a hierarchical encoder. Modules in the encoder
- the encoder 141 includes a segmentation module 201, an aggregation module 202 and a compression module 203.
- the segmentation module 201 applies a segmentation algorithm, which may be selected by the information manager 140, to data previously selected to be encoded.
- the segmentation module 201 generates clusters of data, and keywords and/or other identifiers are established for each cluster.
- the aggregation module 202 applies an aggregation algorithm, which may be selected by the information manager 140, to the clusters to generate summaries for the clusters. Summaries may be provided in XML. The layer of the data package is updated to include the summaries.
- the compression module 203 applies a hierarchal compression strategy determined by the information manager 140. The compression module 203 may apply a compression algorithm selected by the information manager 140. Also, the compression module 203 may apply an archiving method selected by the information manager 140. The archiving method employs the compression algorithm to compress data at different layers of the data package.
- An archiving method is a sequential method.
- raw source data is archived at layer 1 ; the subpackages at layer 1 are archived at layer 2; the subpackages at layer 2 are archived at layer 3; etc.
- a sequential compressed method may be applied that compresses the summaries at the current level and stores them in the archive section of the data package. Only the keywords and other meta data are provided in the data package as uncompressed.
- Another archiving method for minimizing the data package is the differential method.
- the encoder 141 also records relevant compute-time statistics which can assist in the selection of summaries and real-time decoding of archives in the future.
- the decoder 142 is a hierarchal decoder.
- the decoder 142 includes an objective function module 301, a drill-down module 302 and a decompression module 303, as shown in figure 3.
- the objective function module 301 uses an objective function to quantify the trade-offs associated with "goodness of fit" of the retrieved data, decompression time, and other applicable parameters of the data package, to guide the selection of a sequence of traversing the data package.
- the output of the objective function module 301 e.g., a score, may be used by the drill-down module 302 to determine whether to parse meta data and summaries for a lower layer in the hierarchy of the data package or use the data from a current a layer.
- the decompression module 303 decompresses the subpackage or subpackages of interest at the selected layer.
- FIG. 4 illustrates a multi-layer hierarchical data package 400, according to an embodiment.
- the data package includes multiple layers shown as layers 1 through N.
- Each layer is comprised of one or more subpackages.
- layer N has subpackages 430-433;
- layer N-I has subpackages 420-422;
- layer 1 has subpackage 410.
- the number of subpackages shown in each layer is provided by way of example and not limitation, and the number of subpackages in each layer may be different for different data packages.
- the decoder 142 drills down to a lower layer.
- the subpackage 420 is related to the subpackage 430 and the meta data and summary for the subpackage 430 is parsed to determine whether that subpackage contains data of interest for the user. If so, the data is decompressed. Meta data for each subpackage may identify related subpackages in higher or lower levels in the hierarchy to allow for efficiently identifying a related subpackage in another layer for drill down.
- Figures 5A-B illustrates an example of data that may be encoded to form a multi-layer hierarchical data package. In this example, the data is a text document 500.
- Figures 6A-B and 7A-C illustrate code representing at least some of the information in layers in a multi-layer hierarchical data package including 3 layers, according to an embodiment.
- Figures 6A-B and 7A-C illustrate the types of information and examples of information in the data package. It should be noted that data packages can have more or less layers with different numbers of subpackages and different information.
- the data package shown in figures 6A-B and 7A-C is comprised of data encoded from the text document 500 shown in figures 5A-B.
- the data package includes 3 layers.
- Layer 3 is the outer most layer that would be parsed first by the decoder 142.
- Layer 3 represents a layer created from 3 cycles of compression.
- Layer 3 includes the most focused subpackages.
- Layer 2 is an intermediate layer.
- Layer 2 has fewer subpackages having more content and more general content.
- Layer 1 is not shown, but is the inner most layer of the data package and includes the most content and has the broadest scope, e.g., a representation of the original complete data.
- the information manager 140 has established a target of 5 to 1 compression for each of the layers, and a maximum of 3 subpackages for each layer.
- Figures 6A-B show the intermediate layer 2 in the data package, which is labeled 600.
- Each layer also has meta data.
- Meta data for layer 2 is shown as 603 and 604.
- Meta data 603 may include attributes about the computing resources for the device, connectivity, bandwidth, etc.
- Meta data 603 provides information about the hierarchal compression strategy for the layer and data package.
- Meta data 603 may include the maximum compression ratio, the maximum number of subpackages per layer, the archival method, etc.
- the subpackages also include meta data.
- Meta data 605 and 606 are shown for the subpackages 1 and 2 respectively, and includes information regarding the segmentation, aggregation and compression used.
- segmentation includes the identification of sections of the overall data set that relate to specific themes or topic clusters.
- a number of algorithms may be used to perform such clustering.
- segmentation can be accomplished by applying a data mining method, e.g., rule induction, classification based on association (CBA), etc.
- the meta data for segmentation may identify the clustering algorithm used to create the clusters.
- Aggregation creates the summaries for the subpackages.
- the aggregation creates text summaries for the source document shown in figures 5A-B6.
- the summaries correspond to the clusters, which may be topics of interest, identified in the segmentation.
- the meta data for aggregation may identify the aggregation method used to create the summaries, such as a sentence extraction method.
- aggregation may be accomplished by creating statistical summaries of data at a given level of stratification, i.e., including one or more segments of the data.
- data may be aggregated by generating explicit numerical relations that summarize a set of data, e.g., by using gene expression programming (GEP), such as described in U.S. Patent 7,127,436, entitled “Gene Expression Programming Algorithm", assigned to Motorola, Inc., which is incorporated by reference in its entirety.
- GEP gene expression programming
- the meta data may also identify the compression algorithm for compressing the document. Compression algorithms generally apply to any set of binary data. However, the information manager 140 may select a compression algorithm that is specifically tuned for good performance with certain types of data, e.g., text-only, JPEG image set, etc.
- the meta data also includes an ID or a link to the compressed data.
- the meta data also includes one or more keywords describing the cluster, which is the topic of interest in this example.
- the cluster for subpackage 1 is described by the keywords "context” and "aware”.
- the subpackages 1 and 2 include summaries 607 and 608 respectively.
- the summaries are created through the aggregation process.
- the summaries help identify whether the data for the subpackage is sufficient for the user or whether to select another subpackage or drill down to another layer.
- the summaries include text from the source document in figures 5A-B that is related to the topic of interest, which represents the cluster described by the keyword(s) for the subpackage.
- the compressed data for layer 2 is shown as 609 in figure 6B.
- layer 2 includes compressed data for lower-level layer 1.
- Meta data 610 for the compressed data may include information for coding the data. This information may be used by the information manager 140 to make coding decisions.
- FIGS 7A-C illustrate layer 3 in the data package.
- Layer 3 includes 3 subpackages, shown as subpackages 3-5 and labeled as 701-703.
- Layer 3 and the subpackages 305 include meta data similar to the meta data described above for layer 2.
- Layer 3 includes meta data 704 and 705.
- the subpackages 3-5 include meta data 706-708 and summaries 709-711.
- the keywords for subpackages 3 and 4 include the keywords for subpackage 1.
- each of the subpackages 3 and 4 includes an additional keyword.
- subpackages 3 and 4 are related to subpackage 1 in the hierarchy, but provide an additional level of detail as to the data in the data package.
- Layer 3 also includes compressed data 712-714. Because the sequential archival method was used, layer 3 includes compressed data for lower- level layers 1 and 2. Other archival methods may store compressed data for the layer with the layer.
- Figure 8 illustrates a flow chart of a method 800 for encoding a multilayer hierarchal data package, according to an embodiment.
- Figure 8 is described with respect to one or more of figures 1 -1C by way of example and not limitation. It will be apparent to one of ordinary skill in the art that the method 800 may be practiced in other systems.
- step 801 data to be encoded is identified. For example, a set of files or some other set of data is selected for encoding. The data may be identified by a user or by other means.
- a hierarchal compression strategy is determined for encoding the data.
- the hierarchal compression strategy may include a target level of compression and preferred compression algorithms or archival methods based on intended recipients.
- the compression strategy may be based on computing resource attributes for devices of intended recipients, negotiated policies and/or the number of topics or clusters.
- the selected data is divided into clusters.
- a segmentation algorithm may be used to generate the clusters.
- step 804 summaries are generated for the clusters, for example, using an aggregation algorithm.
- the summaries describe information in the clusters and may be used to identify information of interest to a user during decoding.
- step 805 the selected data associated with each cluster is compressed according to the hierarchical compression strategy. This may include implementing an archiving method, e.g., sequential, sequential compressed, differential, etc., to compress the data. Compression meta data may be generated and stored, such as compute time statistics that can be used for optimizing the decoding process in real-time.
- a layer in the data package is created including the summaries, meta data and compressed data. Examples of layers and the meta data are shown in figures 6A-B and 7A-C, and each layer includes one or subpackages.
- a determination is made as to generate another layer. For example, the information manager 140 compares meta data for each subpackage to the hierarchal compression strategy selected by the information manager 140. If one or more of the desired compression rate, summary sizes, or keyword-based specificity of summaries, has been achieved, then the encoding is completed. If not, then steps 801-807 are repeated to create one or more other layers.
- Figure 9 illustrates a block diagram of a general purpose computer system 900 that is operable to be used as a platform for the components of the system 100 described above.
- the system 900 may be representative of a platform for the server 110 or one or more of the user devices 120 and 130. Components may be added or removed from the general purpose system 900 to provide the desired functionality.
- the system 900 includes a processor 902, providing an execution platform for executing software. Commands and data from the processor 902 are communicated over a communication bus 903.
- the system 900 also includes a main memory 906, such as a Random Access Memory (RAM), where software may reside during runtime, and a secondary memory 908.
- the secondary memory 908 may include, for example, a nonvolatile memory where a copy of software is stored.
- the secondary memory 908 also includes ROM (read only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM).
- the system 900 includes I/O devices 910.
- the I/O devices may include a display and/or user interfaces comprising one or more I/O devices 910, such as a keyboard, a mouse, a stylus, speaker, and the like.
- a communication interface 913 is provided for communicating with other components.
- the communication interface 913 may be a wired or a wireless interface.
- the communication interface 913 may be a network interface.
- the components of the system 900 may communicate over a bus 909.
- One or more of the steps of the methods described above and other steps described herein and software described herein may be implemented as software embedded or stored on a computer readable medium.
- the steps may be embodied by a computer program, which may exist in a variety of forms both active and inactive.
- they may exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats for performing some of the steps when executed.
- Modules include software, such as programs, subroutines, objects, etc. Any of the above may be stored on a computer readable medium, which include storage devices and signals, in compressed or uncompressed form.
- Examples of suitable computer readable storage devices include conventional computer system RAM (random access memory), ROM (read only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), and magnetic or optical disks or tapes.
- Examples of computer readable signals are signals that a computer system hosting or running the computer program may be configured to access, including signals downloaded through the Internet or other networks. Concrete examples of the foregoing include distribution of the programs on a CD ROM or via Internet download. In a sense, the Internet itself, as an abstract entity, is a computer readable medium. The same is true of computer networks in general.
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US11/929,623 US20090210436A1 (en) | 2007-10-30 | 2007-10-30 | Encoding a hierarchical multi-layer data package |
PCT/US2008/080912 WO2009058650A1 (en) | 2007-10-30 | 2008-10-23 | Encoding a hierarchical multi-layer data package |
Publications (2)
Publication Number | Publication Date |
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EP2208131A1 true EP2208131A1 (en) | 2010-07-21 |
EP2208131A4 EP2208131A4 (en) | 2011-01-12 |
Family
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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EP08845183A Withdrawn EP2208131A4 (en) | 2007-10-30 | 2008-10-23 | Encoding a hierarchical multi-layer data package |
Country Status (5)
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US (1) | US20090210436A1 (en) |
EP (1) | EP2208131A4 (en) |
KR (2) | KR20130018367A (en) |
CA (1) | CA2703577A1 (en) |
WO (1) | WO2009058650A1 (en) |
Families Citing this family (8)
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US9219503B2 (en) * | 2013-10-16 | 2015-12-22 | Avago Technologies General Ip (Singapore) Pte. Ltd. | Systems and methods for multi-algorithm concatenation encoding and decoding |
US9608664B2 (en) | 2013-12-30 | 2017-03-28 | International Business Machines Corporation | Compression of integer data using a common divisor |
US9564136B2 (en) * | 2014-03-06 | 2017-02-07 | Dts, Inc. | Post-encoding bitrate reduction of multiple object audio |
US9628107B2 (en) | 2014-04-07 | 2017-04-18 | International Business Machines Corporation | Compression of floating-point data by identifying a previous loss of precision |
US9350384B2 (en) | 2014-09-30 | 2016-05-24 | International Business Machines Corporation | Hierarchical data compression and computation |
US9959299B2 (en) | 2014-12-02 | 2018-05-01 | International Business Machines Corporation | Compression-aware partial sort of streaming columnar data |
US10909078B2 (en) | 2015-02-25 | 2021-02-02 | International Business Machines Corporation | Query predicate evaluation and computation for hierarchically compressed data |
CN110929024B (en) * | 2019-12-10 | 2021-07-02 | 哈尔滨工业大学 | Extraction type text abstract generation method based on multi-model fusion |
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2008
- 2008-10-23 CA CA2703577A patent/CA2703577A1/en not_active Abandoned
- 2008-10-23 EP EP08845183A patent/EP2208131A4/en not_active Withdrawn
- 2008-10-23 WO PCT/US2008/080912 patent/WO2009058650A1/en active Application Filing
- 2008-10-23 KR KR1020137000091A patent/KR20130018367A/en not_active Application Discontinuation
- 2008-10-23 KR KR1020107009558A patent/KR20100077001A/en active IP Right Grant
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Also Published As
Publication number | Publication date |
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EP2208131A4 (en) | 2011-01-12 |
CA2703577A1 (en) | 2009-05-07 |
KR20100077001A (en) | 2010-07-06 |
KR20130018367A (en) | 2013-02-20 |
WO2009058650A1 (en) | 2009-05-07 |
US20090210436A1 (en) | 2009-08-20 |
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